Lately, I’ve been trying to get a bead on culture and the effect culture has on Project/Technology ROI and Implementation Cost. The research I have uncovered suggests something very striking. What I mean is that most, if not all, of the Culture Assessment tools I examined have a specific view point. That viewpoint is simple: That if you interview people with a certain list of questions, you can come to find the culture of your organization. The results are graphs, charts, and definitions. At the time I was examining these products, I had no problem with them. As time went on, I came to realize that there might be something missing.
On the surface, these methodologies seem like magic. I could see why they sold. But as I thought about it and reflected on my experience in corporate consulting, I came to a conclusion…
Most of the methodologies out there right now really don’t take the whole of their organization into consideration. They just think they do.
Interestingly enough, they claim to give the whole picture of the culture of the organization supported by their results, but I don’t believe they have been very successful up to this point . In my opinion, this is what the current Organizational Assessments promise to show you:
Here’s the kicker: This view commonly says there is ONE culture that describes 100% of the organization. That the whole of the organization has the same culture. I’m not sure that this is an accurate depiction of what is really going on in reality.
My position on why this occurs? Most culture assessments methodologies come to this conclusion is because of four main factors:
- Leadership is most often interviewed in a culture assessment which creates a bias.
- There is no cross-pollination of information before the survey results are tallied.
- Employees (and people in general) are trained and conditioned to assume culture is a top-down mandate.
- The current dominant POV is that culture is bound by the borders of the organization.
Well, I have news…Good news for some and maybe not for others.
Leadership, while it has influence, does not a culture make.
You see, there is another reason for organizational strife, bad ROI, silos, and poor project management in general. It is not just competition for scare organizational resources. It is also the competition between various, border-less, and unconstrained cultures vying for survival and influence. Cultures do not stay within departments (or even organizations)! Assessing only leadership’s perception of culture does not give a clear picture of how software, process, or procedure should be implemented. Each culture has its own way of integrating new information in the form of technological change and a more successful implementation team will understand and put this into practice.
What we really have in an organization are nested cultural nodes, or simply “Pocket Cultures“, which are all interacting in a very complex way. Through this interaction, the true organizational culture emerges. Some Pocket Cultures, like personalities, can be dominant in the organization. This does not mean the dominant culture is the correct one. It is just the one that uses strategic means to keep its position as dominant.
So, what does all this mean?
- Current organizational assessment tools are most likely ill-equipped to deal with a reality which takes this complex cultural interplay into consideration.
- Executives can expect a higher adoption rate and ROI if they understand the concept of Pocket Cultures.
- Implementation Project Managers should lead with an assessment of Pocket Cultures to find the best entry-point into the organization, giving them a much higher success rate.
Well, that’s my rant. I can see I have a lot of work to do on this idea. I’ll bring some of the big brains I know together to mull this over. Firstly, I will will be working on developing an assessment which takes pocket cultures into consideration. Everything after that is a hazy future-fog, but I bet you that it is fun out there!
For more on Organizational Types, see: Organizational Types or Wikipedia.
So, I was going to write about unemployment and how the job market has changed, but I got scooped by an amazing article by Drake Bennett called The end of the office…and the future of work. It is a great look into the phenomenon of Structural Unemployment. The analysis is very timely, but can go much deeper. Drake, if you plan on writing a book here’s your calling. There’s lots of good stories written on this subject out there by giants such as Jeremy Rifkin, John Seely Brown, Kevin Kelly, and Marshall Brain.
While reeling from the scoop, depressed and doing some preliminary market research, I happened upon a gem of a blog post by none other than our favorite search company, Google. Before proceeding on in my post, I do recommend that you do read the blog post by Steve Baker, Software Engineer @ Google. I think he does an excellent job describing the problems Google is currently having and why they need such a powerful search quality team.
Here’s what I got from the Blog post: Google, though they really want to have them, cannot have fully automated quality algorithms. They need human intervention…And A LOT OF IT. The question is, why? Why does a company with all of the resources and power and money that Google has still need to hire humans to watch over search quality? Why have they not, in all of their intelligent genius, not created a program that can do this?
Because Google might be using methods which sterilize away meaning out of the gate.
Strangely enough, it may be that Google’s core engineer’s mind is holding them back…
We can write a computer program to beat the very best human chess players, but we can’t write a program to identify objects in a photo or understand a sentence with anywhere near the precision of even a child.
This is an engineer speaking, for sure. But I ask you: What child do we really program? Are children precise? My son falls over every time he turns around too quickly…
The goal of a search engine is to return the best results for your search, and understanding language is crucial to returning the best results. A key part of this is our system for understanding synonyms.
We use many techniques to extract synonyms, that we’ve blogged about before. Our systems analyze petabytes of web documents and historical search data to build an intricate understanding of what words can mean in different contexts.
Google does this using massive dictionary-like databases. They can only achieve this because of the sheer size and processing power of their server farms of computing devices. Not to take away from Google’s great achievements, but Syntience’s experimental systems have been running “synthetic synonyms” since our earliest versions. We have no dictionaries and no distributed supercomputers.
As a nomenclatural [sic] note, even obvious term variants like “pictures” (plural) and “picture” (singular) would be treated as different search terms by a dumb computer, so we also include these types of relationships within our umbrella of synonyms.
Here’s the way this works, super-simplified: There are separate “storage containers” for “picture”, “pictures”, “pic”, “pix”, “twitpix”, etc, all in their own neat little boxes. This separation removes the very thing Google is seeking…Meaning in their data. That’s why their approach doesn’t seem to make much sense to me for this particular application.
The activities of an engineer would be to write code that, in a sense, tells the computer to create a new little box and put the new word in a list of associated words. Shouldn’t the computer be able to have some sort of continuous, flowing process which allows it to break out of the little boxes and allow for some sort of free association? Well, the answer is “Not using Google’s methods.”.
You see, Google models the data to make it easily controllable…actually for that and for many, MANY other reasons. But by doing so, they have put themselves in an intellectually mired position. Monica Anderson does a great analysis of this in a talk on the Syntience Site called “Models vs. Patterns”.
So, simply and if you please, rhetorically:
How can computer scientists ever expect a computer to do anything novel with data when there is someone (or some rule/code) telling them precisely what to do all the time?
Kind of constraining…I guess that’s why they always start coding at the “command line”.
Now that we have discussed the fundamental strengths and weaknesses of a Push-based organization, I think it is safe to start discussing the Pull-based design and how it is different from the mechanical Push organization. We’ve already said that Push organizations are efficient, results-oriented, and structured. This is the case for every Push organization and every one of the “wanna-be-Pull” designs that have been tried in the last decade or so.
Some of these pseudo-Pulls are the Functional, Divisional, and the Matrix organization. Funny thing is, even though they pose as innovative, they all pretty much end up being rigid and fragile in the new paradigm. So, if the Push organization was mechanically structured, how can one describe a system which does not depend upon the ideas of top-down management?
It means throwing away a lot of things you were taught about hierarchical organizations.
Let’s start by looking at the strengths and weaknesses of a Pull-based organization and maybe you will start to see what needs to be checked at the door:
|Flexibility Against the Unexpected||Inefficient|
|Requires No Theories (or Consultants)||Burns Resources|
|Environment is Assumed Ideal||Unpredictable Outcomes|
|Embraces Innovation and Invention||Not Easily Measurable and Transparent|
|Self-Reinforces Against Hidden Risks||Can Be Irrational|
|Never Gets “Too Big to Fail”||Not Always Repeatable|
|Black, White, Grey, Blue, Red…etc.||No Plug and Play Models|
|Handles Complexity Well||Highly Unstructured|
|Creates Emergent Value||Knowledge Agnostic|
If it appears that I have flipped the script from the other Strengths & Weaknesses table, you are not mistaken. One of the wonders of this great and infinitely open system called “reality” are these fun little paradoxes which appear to be opposites. The true embrace begins however, when we stop seeing them as opposites and start to see them as equals to be applied where their strengths are needed.
The greatest future (and present) leaders will be wise enough to effectively choose a Push or Pull approach for the operation at hand, all the while being comfortable with the strange loops their decisions create. It takes a bit of bravery to move into this space and effective leaders will need to be courageous enough to break the mold and abandon the expectations pushed upon them.
If there is a change management plan to all of this it is this: Embrace Paradox and Uncertainty!
Next up: What does a Pull team look like? If it is not “commanded” to form, how does it form?
(NOTE: I have abandoned the discussion of individual personality in this series of posts. I think that by reading about Push & Pull Organizations you will get who likes to be where. If you don’t, here’s a summary: Push can be considered managing others & Pull is self management. Control-based people like Push…Flaky creatives like Pull.)
My 90-year-old Nana (Paternal Grandmother) is an inventor and her inventions work.
For example, one of my Nana’s inventions is a color-coded flagging system for dog doo-doo left in her front yard by neighbors who don’t clean up their pet’s mess. The system is simple. If it is a fresh dog dropping the marker (A tomato stake and colored plastic bag.) is yellow which warns people not to step there lest they need to clean their shoe of said droppings.
As the dropping starts to “mature” (Or get dried out and easier to pick up.), my Nana replaces the yellow flag with an orange one to inform her which ones are ready to pick up that week. These flags, in concert with a systematic lawn-checking walking pattern done on a weekly basis, keeps shoes clean and dog doo-doo marked for elimination.
Did I mention each flag has “Doo-Doo” written on the plastic in blue Sharpie?
The invention of this system is made real by the operations of the mind of my Nana. This process of invention is inherently “Model Free”, meaning that my Nana did not need to know differential equations or string theory to make her idea manifest. What’s “Model Free” mean anyway?
“Model Free” means you do not need a PhD or to know the hard sciences like Physics to solve a problem. You just observe the problem and a solution comes to you.
Many in the fields of Economics, Neurology, and Computer Science have been trying to come up with ways to solve complex problems using descriptive models. However, nothing seems to work as well as good ole fashion gray matter. If you wonder why this is, don’t think it is because these complex problems cannot be solved. We just need to change our perspective to understand the operations of the “Model Free” so we might expand our tool sets to encompass the methods of Creation, Natural Construction, Emergence, and Complexity. Innovation also falls in this category…To a point.
So is innovation “Model Free”?
Since innovation encompasses ideas and inventions applied successfully in practice, I have to say not so much. Innovation can be “Model Free” if it is implemented in a Model Free environment, but innovation quickly becomes subject to the introduction of models when the innovation is tied to a corporate agenda or to the scientific method. Using the example of my Nana, she successfully implemented a working invention which made her life much easier. “Model Free” innovation can quickly become “Model Rich” innovation as soon as someone says:
PROVE IT! Tell me how this makes life easier! (Or how it saves/makes me money…)
In the case of business or science, this means MEASUREMENT. So, I’m expanding the definition of “Model Free” to include the absence of measurement. As soon as an innovation starts including aspects of measurement, it ceases to be completely “Model Free”. Can you see the guys in the white lab coats and the consulting khakis approaching a 90 year old woman and attempting to get her to prove the “value proposition” associated with her design? Ridiculous, but measurement in its ivory tower has overwhelmed natural processes of creation and has brought us to the extreme brink.
When did we start believing measurement and models are the the source of invention and innovation, not the other way around?
It is a great confusion, imho.